A hierarchy machine: Learning to optimize from nature and humans

Complexity

Volumn 8, Issue 5, 2003, Pages 36-45

  Pelikan, Martin ^a   Goldberg, David E ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Bayesian optimization algorithm; Decomposable problems; Hierarchical traps

Indexed keywords

BOA;

EID: 2142762712     PISSN: 10762787     EISSN: None     Source Type: Journal    
DOI: 10.1002/cplx.10103     Document Type: Article

References (34)

1. Simon, H. The Sciences of the Artificial; MIT Press: Cambridge, 1968.
2. Holland, J.H. Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI, 1975.
3. Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison-Wesley: Reading, MA, 1989.
4. Rechenberg, I. Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution; Frommann-Holzboog: Stuttgart, 1973.
5. Baluja, S. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Tech. Rep. CMU-CS-94-193; Carnegie Mellon University, Pittsburgh, PA, 1994.
6. Pelikan, M.; Goldberg, D.E.; Lobo, F. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 2002, 21(1), 5-20.
7. Larrañaga, P.; Etxeberria, R.; Lozano, J.A.; Sierra, B.; Inza, I.; Peña, J.M. A review of the cooperation between evolutionary computation and probabilistic graphical models. In: Proceedings of the Second Symposium on Artificial Intelligence, CIMAF 99, Havana, Cuba, 1999; pp 314-324.
8. Pearl, J. Probabilistic reasoning in intelligent systems: Networks of plausible inference; Morgan Kaufmann: San Mateo, CA, 1988.
9. Buntine, W.L Theory refinement of Bayesian networks. In: Uncertainty in Artificial Intelligence, Proceedings of the Seventh Conference, UAI 91; San Mateo, CA, 1991; pp 52-60.
10. Heckerman, D.; Geiger, D.; Chickering, D.M. Learning Bayesian networks: The combination of knowledge and statistical data; Machine Learning 1995, 20(3), 197-243.
11. Pelikan, M.; Goldberg, D.E.; Cantú-Paz, E. BOA: The Bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-99; Morgan Kaufmann: San Francisco, CA, 1999; pp 525-532.
12. Mühlenbein, H.; Mahnig, T.; Ochoa, A. Schemata, distributions and graphical models in evolutionary optimization; Journal of Heuristic 1999, 5, 215-247.
13. Etxeberria, R.; Larrañaga, P. Global optimization using Bayesian networks. In: Proceedings of the Second Symposium on Artificial Intelligence, CIMAF 99, Havana, Cuba, 2002; pp 332-339.
14. Harik, G. Linkage learning via probabilistic modeling in the ECGA. IlliGAL Report No, 99010; University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1999.
15. Kargupta, H.; Sarkar, K. Function induction, gene expression, and evolutionary representation construction; Complex Systems 1999, 3, 153-171.
16. Bosman, P.; Thierens, D. Linkage information processing in distribution estimation algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO-99; Morgan Kaufmann: San Francisco, CA, 1999; pp 60-67.
17. Pelikan, M.; Goldberg, D.E.; Cantú-Paz, E. Linkage learning, distribution estimation, and Bayesian networks; Evolutionary Computation, 2000, 8(3), 311-341.
18. Sastry, K. Efficient atomic cluster optimization using a hybrid extended compact genetic algorithm with seeded population. IlliGAL Report No. 2001018; University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2001.
19. Chickering, D.M.; Heckerman, D.; Meek, C. A Bayesian approach to learning Bayesian networks with local structure. In: Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence, UAI 97; Morgan Kaufmann: Providence, RI, 1997; pp 80-89.
20. Friedman, N.; Goldszmidt, M. Learning Bayesian networks with local structure. In: Graphical Models; Jordan, M.I., Ed.; MIT Press: Cambridge, MA, 1999; pp 421-459.
21. Harik, G. Finding multimodal solutions using restricted tournament selection. In: Proceedings of the Sixth International Conference on Genetic Algorithms, ICGA-95; Morgan Kaufmann: San Francisco, CA, 1995; pp 24-31.
22. Watson, R.A.; Hornby, G.S.; Pollack, J.B. Modeling building-block interdependency. Parallel Problem Solving From Nature (PPSN V), 1998; pp 97-106.
23. Goldberg, D.E. Four keys to understanding building-block difficulty. Presented in Project FRACTALES Seminar at I.N.R.I.A. Rocquencourt, Le Chesney, Cedex, June 15, 1998.
24. Deb, K.; Goldberg, D.E. Sufficient conditions for deceptive and easy binary functions; Ann Math Artif Intell 1994, 10, 385-408.
25. Pelikan, M. Bayesian optimization algorithm: From single level to hierarchy. Ph.D. Dissertation, Departmert of Computer Science, University of Illinois at Urbana-Champaign, IL, 2002.
26. Pelikan, M.; Goldberg, D.E.; Cantú-Paz, E. Bayesian optimization algorithm, population sizing, and time to convergence. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2000; Morgan Kaufmann: San Francisco, CA, 2000; pp 275-282.
27. Pelikan, M.; Sastry, K.; Goldberg, D.E. Evolutionary Algorithms + Graphical Models = Scalable Black-Box Optimization. IlliGAL Report No. 2001029; University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2001.
28. Sastry, K. Efficient atomic cluster optimization using a hybrid extended compact genetic algorithm with seeded population. IlliGAL Report No. 2001018; University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2001.
29. Arst, R.; Minsker, B.S.; Goldberg, D.E. Comparing Advanced Genetic Algorithms and Simple Genetic Algorithms for Groundwater Management. In: American Society of Civil Engineers (ASCE) Environmental & Water Resources Institute (EWRI) 2002 Water Resources Planning & Management Conference, Roanoke, VA, 2002.
30. Sacerdoti, E.D. The nonlinear nature of plans. In: Proceedings of the Fourth Annual International Joint Conference on Artificial Intelligence; Tbilisi/Georgia, USSR, 1975; pp 206-214.
31. Stefik, M. Planning with constraints (MOLGEN: Part 1); Artif Intell 1981, 16(2), 111-140.
32. Stefik, M. Planning and meta-planning (MOLGEN: Part 2); Artif Intell 1981, 16(2), 141-170.
33. Koza, J. Genetic programming: On the programming of computers by means of natural selection; MIT Press, Cambridge, MA, 1992.
34. Koza, J. Genetic programming it: Automatic discovery of reusable programs; MIT Press, Cambridge, MA, 1994.